Conclusions and Future Directions

This book provided detailed state-of-the-art developments on the emerging socio-inspired metaheuristic technique of Cohort Intelligence (CI). The motivation of the methodology is also discussed in detail. The methodology was successfully tested and validated by solving several unconstrained problems...

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Published inCohort Intelligence: A Socio-inspired Optimization Method Vol. 114; pp. 131 - 134
Main Authors Krishnasamy, Ganesh, Kulkarni, Anand Jayant, Abraham, Ajith
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2016
Springer International Publishing
SeriesIntelligent Systems Reference Library
Subjects
Artificial intelligence
Constraint Handling
Travel Salesman Problem
Unmanned Aerial Vehicle
Vehicle Route Problem
Online AccessGet full text
ISBN3319442538
9783319442532
ISSN1868-4394
1868-4408
DOI10.1007/978-3-319-44254-9_10

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Abstract This book provided detailed state-of-the-art developments on the emerging socio-inspired metaheuristic technique of Cohort Intelligence (CI). The motivation of the methodology is also discussed in detail. The methodology was successfully tested and validated by solving several unconstrained problems with different modalities and dimensions. The solution quality was quite promising and encouraging in terms of objective function, robustness, avoidance of local minima, computational time and function evaluations. The effect of each individual parameter such as sampling interval reduction factor, number of candidates and number of variations on the computational performance was also tested.
AbstractList This book provided detailed state-of-the-art developments on the emerging socio-inspired metaheuristic technique of Cohort Intelligence (CI). The motivation of the methodology is also discussed in detail. The methodology was successfully tested and validated by solving several unconstrained problems with different modalities and dimensions. The solution quality was quite promising and encouraging in terms of objective function, robustness, avoidance of local minima, computational time and function evaluations. The effect of each individual parameter such as sampling interval reduction factor, number of candidates and number of variations on the computational performance was also tested.
Author Kulkarni, Anand Jayant
Krishnasamy, Ganesh
Abraham, Ajith
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StartPage 131
SubjectTerms Artificial intelligence
Constraint Handling
Travel Salesman Problem
Unmanned Aerial Vehicle
Vehicle Route Problem
Title Conclusions and Future Directions
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